Cellular Development Follows the Path of Minimum Action
This provides interpretable metrics for cellular fate decisions, addressing a fundamental problem in biology, though it appears incremental as it applies existing methods to new data.
The paper tackles the problem of understanding the underlying principles of cellular development by proposing that it follows paths of least action, and introduces a computational framework using Transformers to model this, enabling precise quantification of entropy production, information flow curvature, and local irreversibility in single-cell RNA data.
Cellular development follows a stochastic yet rule-governed trajectory, though the underlying principles remain elusive. Here, we propose that cellular development follows paths of least action, aligning with foundational physical laws that govern dynamic systems across nature. We introduce a computational framework that takes advantage of the deep connection between the principle of least action and maximum entropy to model developmental processes using Transformers architecture. This approach enables precise quantification of entropy production, information flow curvature, and local irreversibility for developmental asymmetry in single-cell RNA sequence data. Within this unified framework, we provide interpretable metrics: entropy to capture exploration-exploitation trade-offs, curvature to assess plasticity-elasticity dynamics, and entropy production to characterize dedifferentiation and transdifferentiation. We validate our method across both single-cell and embryonic development datasets, demonstrating its ability to reveal hidden thermodynamic and informational constraints shaping cellular fate decisions.